comparison toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_hmm_bnet.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
+% MK_HMM_BNET Make a (static) bnet to represent a hidden Markov model
+% bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying)
+%
+% T = num time slices
+% Q = num hidden states
+% O = size of the observed node (num discrete values or length of vector)
+% cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete
+% param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node
+
+N = 2*T;
+dag = zeros(N);
+%hnodes = 1:2:2*T;
+hnodes = 1:T;
+for i=1:T-1
+  dag(hnodes(i), hnodes(i+1))=1;
+end
+%onodes = 2:2:2*T;
+onodes = T+1:2*T;
+for i=1:T
+  dag(hnodes(i), onodes(i)) = 1;
+end
+
+if cts_obs
+  dnodes = hnodes;
+else
+  dnodes = 1:N;
+end
+ns = ones(1,N);
+ns(hnodes) = Q;
+ns(onodes) = O;
+
+if param_tying
+  H1class = 1; Hclass = 2; Oclass = 3;
+  eclass = ones(1,N);
+  eclass(hnodes(2:end)) = Hclass;
+  eclass(hnodes(1)) = H1class;
+  eclass(onodes) = Oclass;
+else
+  eclass = 1:N;
+end
+
+bnet = mk_bnet(dag, ns, 'observed', onodes, 'discrete', dnodes, 'equiv_class', eclass);
+
+hnodes = mysetdiff(1:N, onodes);
+if ~param_tying
+  for i=hnodes(:)'
+    bnet.CPD{i} = tabular_CPD(bnet, i);
+  end
+  if cts_obs
+    for i=onodes(:)'
+      bnet.CPD{i} = gaussian_CPD(bnet, i);
+    end
+  else
+    for i=onodes(:)'
+      bnet.CPD{i} = tabular_CPD(bnet, i);
+    end
+  end
+else
+  bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior
+  bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix
+  if cts_obs
+    bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1));
+  else
+    bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1));
+  end
+end